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使用多热浴对液态水分子间和分子内模式进行建模:机器学习方法。

Modeling Intermolecular and Intramolecular Modes of Liquid Water Using Multiple Heat Baths: Machine Learning Approach.

机构信息

HPC Systems Inc., Nakagyoku, Kyoto 604, Japan.

Department of Chemistry, Kyoto University, Kyoto 606-8502, Japan.

出版信息

J Chem Theory Comput. 2020 Apr 14;16(4):2099-2108. doi: 10.1021/acs.jctc.9b01288. Epub 2020 Mar 20.

DOI:10.1021/acs.jctc.9b01288
PMID:32150682
Abstract

The vibrational motion of molecules in dissipative environments, such as solvation and protein molecules, is composed of contributions from both intermolecular and intramolecular modes. The existence of these collective modes introduces difficulty into quantum simulations of chemical and biological processes. In order to describe the complex molecular motion of the environment in a simple manner, we introduce a system-bath model in which the intramolecular modes with anharmonic mode-mode couplings are described by a system Hamiltonian, while the other degrees of freedom, arising from the environmental molecules, are described by a heat bath. Employing a machine-learning-based approach, we determine not only the system parameters of the intramolecular modes but also the spectral distribution of the system-bath coupling to describe the intermolecular modes, using the atomic trajectories obtained from molecular dynamics (MD) simulations. The capabilities of the present approach are demonstrated for liquid water using MD trajectories calculated from the SPC/E model and the polarizable water model for intramolecular and intermolecular vibrational spectroscopies (POLI2VS) by determining the system parameters describing the symmetric-stretch, asymmetric-stretch, and bend modes with intramolecular interactions and the bath spectral distribution functions for each intramolecular mode representing the interaction with the intramolecular modes. From these results, we were able to elucidate the energy relaxation pathway between the intramolecular modes and the intermolecular modes in a nonintuitive manner.

摘要

在耗散环境(如溶剂化和蛋白质分子)中,分子的振动运动由分子间和分子内模式的贡献组成。这些集体模式的存在给化学和生物过程的量子模拟带来了困难。为了以简单的方式描述环境的复杂分子运动,我们引入了一个体系-浴模型,其中具有非谐模式-模式耦合的分子内模式由体系哈密顿量描述,而其他自由度,源自环境分子,由热浴描述。我们采用基于机器学习的方法,不仅确定了分子内模式的体系参数,还确定了体系-浴耦合的谱分布,以描述分子间模式,使用从分子动力学 (MD) 模拟获得的原子轨迹。我们使用 SPC/E 模型和极化水模型计算的 MD 轨迹演示了本方法在液体水中的能力,用于分子内和分子间振动光谱学(POLI2VS),通过确定描述对称伸缩、不对称伸缩和弯曲模式的分子内相互作用的体系参数,以及每个分子内模式的浴谱分布函数,代表与分子内模式的相互作用。从这些结果中,我们能够以一种非直观的方式阐明分子内模式和分子间模式之间的能量弛豫途径。

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